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Integration of functional precision oncology to immune contexture predicts immunotherapies strategies for mucosal melanomas.

GSE287519 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/03/18 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 30 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
30 samples: 10 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Integrating interferon gamma receptor pathways, antigenicity, and immune contexture as predictors of immunotherapeutic strategies for mucosal melanomasJournal for immunotherapy of cancer · 2026
Summary (written by the submitters)
Mucosal melanomas (MM) arise from mucosal melanocytes at various anatomical sites. These tumors are rare, highly aggressive, and often associated with poor outcomes. Current treatments, including immune checkpoint inhibitors, show limited efficacy in advanced disease. Compared to cutaneous melanomas, there is a lack of data on the immunogenicity and IFN-γ sensitivity of MM.
Read the full summary ↓
In this study, we examine these features in sino-nasal melanoma (SN-MM) cell lines and clinical samples using microscopy and functional genomics. Microscopic analysis reveals that most SN-MM tumors are immune "desert" with few tumor-infiltrating lymphocytes (TILs), a characteristic linked to poor prognosis. Additionally, SN-MM tumors are CD274/PD-L1 negative. SN-MM cell lines express transcripts for melanocytic and cancer testis antigens. They also show normal surface expression of IFN-γ receptors (IFNGR) and maintain the integrity of the IFNGR/JAK/STAT signaling pathway. Transcriptomic and proteomic analyses demonstrate that SN-MM cell lines, as a group, respond to IFN-γ by upregulating genes involved in immune recognition and antigen presentation. In 60% of SN-MM lines, IFN-γ also induces cytotoxic and anti-proliferative effects, the release of CXCL10 and upregulation of CD274/PD-L1. The remaining SN-MM cell lines, characterized by poor differentiation, show refractoriness to these effects, likely due to post-transcriptional regulation. These findings suggest that combining functional precision oncology with immune context analysis could refine our understanding of immune escape mechanisms in MM and improve patient stratification for immune therapies.
Published in
Integrating interferon gamma receptor pathways, antigenicity, and immune contexture as predictors of immunotherapeutic strategies for mucosal melanomas
Monti M, Picinoli S, Bozzola A et al. · Journal for immunotherapy of cancer 2026 · PMID 41791790 · doi:10.1136/jitc-2025-012222
This dataset
Open GSE287519 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE287519_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1213138 and SRA study SRP558564. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41791790) ↗
Study design
10 conditions, mostly in triplicate
SN-MM1 cell line, Untreated Control, 4h, biol ×3 SN-MM1 cell line, IFN-γ Treated, 4h, biol ×3 SN-MM2 cell line, Untreated Control, 4h, biol ×3 SN-MM2 cell line, IFN-γ Treated, 4h, biol ×3 SN-MM3 cell line, Untreated Control, 4h, biol ×3 SN-MM3 cell line, IFN-γ Treated, 4h, biol ×3 SN-MM4 cell line, Untreated Control, 4h, biol ×3 SN-MM4 cell line, IFN-γ Treated, 4h, biol ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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